Intellectual Capital in education a value chain perspective. Manfred Bornemann* Roswitha Wiedenhofer. Structured Abstract

Transcription

1 Intellectual Capital in education a value chain perspective Manfred Bornemann* Intangible Assets Consulting GmbH Dr. Anton Schlossar Weg 16 A-8010 Graz, Austria Roswitha Wiedenhofer FH JOANNEUM University of Applied Sciences Alte Poststrasse 149, A-8020 Graz, Austria * Corresponding author Structured Abstract Purpose Educational institutions are highly regulated and regularly in the focus of political as well as professional reflection for improvement. This paper aims to apply the concept of Intellectual Capital (Edvinsson, 1997, Guthrie 2000) to assess intangible resources as crucial for the quality of educational processes as well as to identify patterns of interdependence between drivers of Intellectual Capital and generic processes of educational institutions (Bornemann, 2007) as a prototype study in Austria. Starting with elementary schools, secondary as well as tertiary levels of education are analyzed and related to each other in order to identify the need to differentiate drivers of Intellectual Capital for each type of school or to apply standardized drivers independent from the operational focus (Alwert et al, 2010). Design/methodology/approach Educational institutions are typically regulated by governmental procedures and hence do not follow entrepreneurial management models. This paper suggests the assumption of a value chain of schools with the pupil or learner as the customer as well as the object of intervention. With this analogy, experiences from the application of Intellectual Capital assessment in value chains of the automotive industry are transferred and applied to education. The methodology of Wissensbilanz made in Germany (BMWI, 2004) is applied in action research oriented prototypes along this value chain (Bornemann et al 2011). Results are consolidated according to procedures for stock noted companies (Alwert 2009). Originality/value Austria officially implemented the legal obligation for Intellectual Capital reporting for universities in The implementation of these procedures was discussed controversially in some articles, and certainly did not come to an end. Up to date, no test on the usefulness of Intellectual Capital assessment in other educational institutions such as elementary schools, secondary schools or professional schools was reported. Based on data from 12 case studies collected over a time frame of 2011 and 2012, this paper will report preliminary insight and call for further research along the value chain of education. 570

2 Practical implications Applying methodologies for Intellectual Capital reporting in educational institutions and integrating the management of these resources along the value chain of education seems to be very beneficial not only for ministries of education as the major common stakeholder but for de-central decision makers as well. Prioritizing scarce resources and systematically observing intangible assets in public as well as privately management educational intuitions contributes to economic improvement and better accomplishment of strategic objectives. Keywords intellectual capital, education, value chain, Wissensbilanz made in Germany Paper type Academic Research Paper We would like to express our gratitude to the authors of the case studies, all of them participants of the master class for knowledge management at the Applied University of Eisenstadt. We would like to express our gratitude to the participants of two projects in the business of continuing education, who need to remain unidentified for reasons of confidentiality. We would like to express our appreciation for the feedback from S. Eschenbach (fh Burgenland), R. Reinhardt (fh Riedlingen) and K. Alwert (AKWB): 1 Introduction and Observations According to high level international sources (OECD, 1998; OECD, 2000; OECD, 2012; EC, 2012), education systems are crucial for the productivity and future prosperity of societies. Large budgets are allocated by national governments as well as private institutions and participants in education processes. All of them rightfully expect a reasonable return from their investments, but regularly, respected sources report troubles, albeit on different levels. The Human Development Report (UNDP/DGVN, 2013) is a source that relates the level of education to human development and documents relevant challenges for significant parts of the world. This supports the ambitions to optimally manage education in order to improve well being. 571

3 Figure 1: Population per nation as function of education and human development index, i.e. a composite index measuring average achievement in three basic dimensions - a long and healthy life, knowledge and a decent standard of living (UNDP/DGVN, 2013). Independent from the relative level, national policies try to align their education systems to the (local) labour market and particularly to demands by leading industries and service providers. Governments (from developed as well as emerging economies) regularly publish strategy papers and plans how they intend to accomplish these targets, but only very few provide insights how they would like to proceed. Even so, elaborated national educational strategy and implementation plans most often cover only a special segment within the educational system (e.g. tertiary education) and beyond that strongly focus on structural and resource related issues within the chosen segment. 1.1 Value chains as an approach to improve results In complex industrial production systems with partially global sourcing strategies, concepts such as supply chains were implemented decades ago and improved ever since. The general benefits of supply chain management approaches support (among others) improvement of operations, increase of profits and an enhanced quality of outcome. Later on, these concepts were also applied to service industries and in a few cases to educational contexts. First suggestions of educational supply chains in tertiary education were provided by O Brien and Dean (1996). Lau (2007) introduced a supply chain management approach to suggest innovative management ideas in higher education with 572

4 the case of University of Hong Kong. Habib (2010) elaborated on Lau towards an integrated model. Beyond the complex logistically centered challenges of manufacturing supply chains, additional services before and after production lead to an added value for the end customer. Focusing on interdependencies of value activities across organisations and optimizing these links to create competitive advantage lead to the concepts of value chains as introduced by Porter (1998). Some of these ideas were applied to education, e.g. by Pathak and Pathak (2010), who map and reconfigure the activities of Porter s value chain model within a single organisation to a higher educational institution aiming to optimise the value added for the customers. Williams (2012) takes a broader perspective by using industrial value chains to derive skill maps for workforce development for selected industries. 1.2 Education as a major driver for productivity The application of value chain ideas to the requirements of a knowledge society (Drucker 1993) were - surprising enough - not yet fully transferred to education systems. In this article, we would like to address an education system as a multi-layered construct of various institutions that provide education, starting with kindergarten, as the first formal contact of a young person with institutional instruction, then preschool and primary school. Obligatory education in the EU regions then continues with secondary education and vocational training. Increasingly higher relative shares of a cohort continue their education with one or two advancements in higher education and complete first college, then university degrees. In the European Union, the Bologna Process (EU, 1999) brought a new framework at the beginning of the New Millennium and introduced Anglo- American concepts of bachelor and master into tertiary education. After completing these education process, the former student regularly enters the labour market and sometimes receives a professional education, either immediately or as an upgrade later on, e.g. as continuing education or lifelong learning concept. Figure 2 illustrates this education process. Figure 2: schematic structure of the value chain in education 573

5 We suggest understanding these development levels of a student in the process of education as a value chain with the pupil and student as the object to be developed in several different but consecutive institutions over several years. As mentioned above, the existing literature applies value chain and supply chain approaches to education mainly on various needs for supplies of education providers or concepts for a single educational level. These approaches are explicitly not the focus of our research. Instead, we would like to reconstruct the development and education of people along a time perspective. As educational careers are slightly different, depending on national context and regulation, we would like to discuss our approach by the case of the Austrian education system, which is compatible to most EU countries. As it would exceed the capacity of this article, we will not cover the details of the Austrian education system (BMUKK 2013). Educational institutions in Austria, as well as in most constituencies, are highly regulated and regularly in the focus of political as well as professional reflection for improvement. In addition to the already mentioned Bologna process, the Austrian Government implemented a pioneering regulation to report the Intellectual Capital of universities on a regular basis as part of the performance monitoring processes (Republic of Austria: 13 Abs. 6 Universitätsgesetz 2002 ). The law is of particular relevance, as it expressed the assumed connection of Intellectual Capital as a relevant driver of national performance. In a similar direction argues Hattie (2008) with his synthesis of over 800 meta-analyses relating to achievement in the education process. 1.3 Intellectual Capital Perspectives in educational value chains This paper aims to apply the concept of Intellectual Capital (Edvinsson, 1997, Amidon 1997, Guthrie 2000) as a relevant driver for performance, not only to tertiary education (universities) but to all the educational processes starting from kindergarten and primary level to lifelong learning. We inquire the need to differentiate drivers of Intellectual Capital for each type of organization in contrast to the opportunity to apply standardized drivers. In any case, they are different from available templates in industry (Alwert et al, 2010). Intellectual Capital is discussed intensely in the literature (representative since 2000: Journal of Intellectual Capital, Reinhardt, R. et al (2001); Choo, C. and Bontis, N. (2002); Mouritsen, J. Et. al. (2002)), with early adopters in academia, industry (Sveiby 1997, Steward, 1997, Dumay, 2009 and Dumay, 2012) and public administrations (The Danish Trade and Industry Council, 1997). Based on the experiences in Scandinavia and later in Austria (Bornemann et al, 2003), the German Federal Ministry for Economy and Technology initiated a multi-year program to measure and manage Intellectual Capital (BMWI 2004). Among the results were a well received Guideline for measuring Intellectual Capital - made in Germany (BMWI 2004) and a toolbox to support large scale implementation of Intellectual Capital Reporting in small and medium sized enterprises. The concept was successfully rolled out all over Germany (see AKWB,

6 for more details). With the EU-funded project InCaS (InCaS 2008) the concept became available all over Europe. Initially intended to improve the performance of SMEs, the Wissensbilanz was applied to other systems as well, such as large stock noted enterprises (ENBW ), regional innovation regions and systems (NOEST, 2004; Wiedenhofer, 2009 and 2012) and clusters (Bornemann 2013, Cadic 2012). The topic Intellectual Capital Reporting is increasingly covered by universities and applied universities as part of their regular management training (e.g. Wirtschafts Universität Wien, Donau Universität Krems, Campus02, FH Burgenland, FH Riedlingen, Technical University Dresden, Fraunhofer Academy) and thus becomes visible for several students as a field of application and research. 2 Challenges 2.1 Research questions As part of these educational activities, several case studies emerged as prototypes as well as regular application of the concept of Intellectual Capital Reporting and Management and provide the qualitative source to investigate the research questions for this paper. 2.2 Data We would like to explore the current status quo of taxonomy for Intellectual Capital in educational institutions for four levels of education (we do not have data for the preschool level): Do we have an established language for Intellectual Capital in Education? With this semantic analysis, we would like to identify the degree of connectedness of IC drivers and exchange between these institutions: Can we construct a value chain perspective? Finally, we would like to investigate the current management of this assumed value chain and explore whether or not the concept of Wissensbilanz made in Germany as the management methodology for intangibles could contribute to improve the overall performance. How could we measure and manage Intellectual Capital in education in order to achieve better results from the value chain and improve general welfare? Based upon current Austrian legislation and in line with international practice, we identified five levels of an educational value chain (see: Figure 2). For each level but preschool or Kindergarten, one or more case studies to measure Intellectual Capital could be accessed for secondary analysis. Three sources of data were utilized, as the authors had not direct access to all institutions: Case studies conducted by the authors as part of their research and consulting activities 575

7 Diploma and master thesis papers of students focussing on Intellectual Capital with at least one semester (4 ECTS) of formal training on the methodology of Intellectual Capital Reporting and additionally more than 350 hours of their own research, and Seminar papers provided by master students with at least one semester (4 ECTS) of formal training on the methodology of Intellectual Capital Reporting. Table 1 covers the distribution of the papers and their relative focus. Twelve case studies involved 55 teachers and administrative staff from the institutions under analysis and thus draw on first hand experience. All participants invested a series of 4 full day workshops with a total of 80 hours of their time into the reports and thus provide very privileged sources of data. Hence, validity as well as reliability of the retrieved statements are supposed to be very high and exceeding the quality of other sources of data, such as (extended) interviews or surveys. All case studies were double checked by the authors. Table 1: distribution of case study sources master seminar master thesis consulting primary 2 2 secondary 3 3 tertiary 1 quartery 2 Restrictions and limitations As this paper reports the findings of an ongoing research project, only one case study for the tertiary level could be considered. To avoid eclectic results, at least one additional case should be covered in a next stage. However, the results of this case in the tertiary level are - according to the author s best knowledge - in line with other results from higher education institutions, but their data could not be used because of methodological limitations and confidentiality reasons. 2.3 Method Wissensbilanz - made in Germany (2004, 2007 and 2013) as well as InCaS (2007) are available as open source guidelines in English. The method builds on 8 steps to come up with a full report as well as management priorities on Intellectual Capital. The process is a team effort with typically not less than 4 and not more than 12 participants. For this paper, the following activities are most relevant: 576

8 Definition of the system boundaries: As the focus of providing Intellectual Capital Statements is on internal management (and - so far not (!) an integrated management approach covering several institutions), the objective and limit of the case studies was typically a distinct school or institution. Definition of IC drivers: Even though some templates for drivers of Intellectual Capital are available in the guideline as well as in the toolbox, the participants were encouraged to identify the drivers of Intellectual Capital within their system and depending on the individual strategic objectives. The Qualitative assessment of Intellectual Capital is based on three dimensions. Two of them cover the present (status quo of quantity and quality of a driver), one focuses on the management and future development (systematic management). All three dimensions are measured according to the strategic fit on a scale from 0 to 100. Indicators to support and supplement the qualitative assessment are part of the method but were not included in this study. This is because of so far not available standard definitions for indicators (including a standardized mathematical definition and standardized procedures to collect the data) with a binding definition for all institutions covered in this study. But even a standard definition would not guarantee availability or access to the data. Business dynamics or cross impact analysis: All case studies include an analysis of cross-interdependence to assess the impact of an improvement (change project) for one driver on all the others. This analysis allows to model causality patterns (see Sterman, 2000; Vester, 1999; Senge, 1990) and thus supports organizational learning as well as prioritization for change projects. This paper does not focus on individual results on the status quo of Intellectual Capital or management actions focussing on the elimination of identified bottlenecks for further organizational development. This paper does not cover internal or external communication issues. Based on this methodological setting and the data obtained from 12 case studies, we would like to explore the research questions as mentioned before. 3 Analysis and Findings 3.1 Do we have an established language for Intellectual Capital in Education? Based on 12 case studies, we conducted a semantic analysis for the drivers of Intellectual Capital. Most of the authors adhered to the Guideline for Intellectual Capital Reporting and identified 3-5 drivers for each of the Intellectual Capital categories of Human Capital, Structural Capital and Relational Capital. 577

9 Table 2: distribution of drivers of IC for each case System A B C D E F G H I J K L Number of HC drivers Number of SC drivers Number of RC drivers Number of IC drivers

10 Table 2 covers the number of drivers for each system as well as the aggregate number in the bottom line. We can see an almost normal distribution with the minimum of 9 drivers - the minimum of the recommended 3 drivers for each category. The highest number is 22 drivers. Because of the comparative large gap of 7, this constitutes a clear exception. In 579

11 Table 2 we see that Human Capital was covered with almost double the amount of drivers. This can be explained by the differentiation of two groups of employees in this particular institution (teachers and educators). A similar explanation applies for Relational Capital, as the author of this case investigated various stakeholders. However, the cases are acceptably similar with regard to the level of differentiation in Intellectual Capital. In order to test for an established set of drivers for Intellectual Capital, we identified the individual drivers for Human Capital and for Relational Capital (Structural Capital provides similar results, but they are not covered because of space restrictions). Fachkompetenz 6 Fachliche Qualifikation (inkl. Weiterbildung) 1 Fachliche Kompetenz 3 Methodisches und didaktisches Fachwissen der Lehrer 1 Didaktische und Methodenkompetenz 1 Qualifikation der Lehrkräfte 1 MitarbeiterInnenqualifikation 1 Erfahrung der Lehrkräfte 1 Fachkompetenz und method. Kompetenz 1 Fortbildung 1 allgemeine Mitarbeiterkompetenzen entwickeln 1 Fachqualifikationen für die Akademie aufbauen 1 FACHKOMPETENZ 19 Mitarbeitermotivation und Zufriedenheit 1 Motivation der LehrerInnen 1 Mitarbeitermotivation 3 Motivation 1 Motivation der Lehrkräfte 1 Mitarbeiterzufriedenheit 2 Identifikation 1 Engagement der Lehrer 1 Engagement der Erzieher 1 MITARBEITERMOTIVATION 12 soziale Kompetenz 5 Sozialkompetenz 4 Selbst- und Sozialkompetenz 1 Soft Skills 1 Sozialkompetenz der Lehrkräfte 1 SOZIALKOMPETENZ 12 Führungskompetenz 5 Führungskompetenz weiterentwickeln 1 Führungsstärke der Direktion und der Lehrer 1 Führungsstärke der Erzieher 1 FÜHRUNGSKOMPETENZ 8 580

12 Persönliche Einstellung 1 Authentisches Christsein 1 Veränderungsfähigkeit unterstützen 1 SONSTIGE 3 and Table 5 in the appendix contain all drivers in German language - the very subtle differences are difficult to translate and would not make a difference for this analysis. From Fachkompetenz 6 Fachliche Qualifikation (inkl. Weiterbildung) 1 Fachliche Kompetenz 3 Methodisches und didaktisches Fachwissen der Lehrer 1 Didaktische und Methodenkompetenz 1 Qualifikation der Lehrkräfte 1 MitarbeiterInnenqualifikation 1 Erfahrung der Lehrkräfte 1 Fachkompetenz und method. Kompetenz 1 Fortbildung 1 allgemeine Mitarbeiterkompetenzen entwickeln 1 Fachqualifikationen für die Akademie aufbauen 1 FACHKOMPETENZ 19 Mitarbeitermotivation und Zufriedenheit 1 Motivation der LehrerInnen 1 Mitarbeitermotivation 3 Motivation 1 Motivation der Lehrkräfte 1 Mitarbeiterzufriedenheit 2 Identifikation 1 Engagement der Lehrer 1 Engagement der Erzieher 1 MITARBEITERMOTIVATION 12 soziale Kompetenz 5 Sozialkompetenz 4 Selbst- und Sozialkompetenz 1 Soft Skills 1 Sozialkompetenz der Lehrkräfte 1 SOZIALKOMPETENZ 12 Führungskompetenz 5 Führungskompetenz weiterentwickeln 1 Führungsstärke der Direktion und der Lehrer 1 Führungsstärke der Erzieher 1 FÜHRUNGSKOMPETENZ 8 581

13 Persönliche Einstellung 1 Authentisches Christsein 1 Veränderungsfähigkeit unterstützen 1 SONSTIGE 3 in the appendix we see that the authors used 33 different names for a cluster of only 5 major drivers of Human Capital. Table 5 in the appendix offers 30 different drivers for Relational Capital, but needs 7 subsets, one a residual, as some drivers could not be assigned to a major driver. Even more scattered are the drivers for Structural Capital which needs up to 12 different subsets. We have to attest a highly differentiated set of drivers for each level of the value chain with a relatively uncontested set for Human Capital (5 drivers) and diverse Structural (12) and Relational (7) Capital. This supports the hypothesis that Structural Capital constitutes a major competitive advantage of an organization as employees are free to move and - at least in small organizations - as employees regularly control the relations to external groups. From the perspective of a value chain analysis, Relational Capital is particularly relevant. Hence, we would like to investigate the drivers of Relational Capital with some qualitative details: Customer Capital (Beziehungen zu Eltern, Schülern und Alumni (Kunden)): While all SMEs agree upon customer relations (Alwert et al 2011) this is not the case in education. Of 12 case studies only 6 explicitly used the term customer relations, while several other stakeholders were identified as crucial to accomplish their strategic objectives: pupils, alumni, parents and custodians were considered as relevant. However, no case identified industry as the likely future employer of successful graduates as relevant to be included in the list of drivers for Relational Capital. And no case considered (future) taxpayers - as distinct from parents - as a relevant stakeholder of the public funded education sector. This maybe reveals something about the strategic positioning of the case-study teams that provide the reports Relations to cooperation partners (Beziehungen zu Kooperationspartnern) 10 out of 12 case studies refer to some external parties as privileged partners. This term comes very close to what could be understood as a connector between institutions from the same and maybe even different levels of the value chain in education. (In the SME context we see this driver comparatively frequent. Relations to cooperation partners refers to organizations that have some impact on each other for various reasons such as sharing (production-) capacity, access to technology or markets or even joint activities such as research and development that are too complex for only one to handle.) When we drill down to the definitions behind the headlines, we learn that most case studies provide only ambiguous hints, whom they consider as distinct from other suppliers and for what reasons. Educating institutions sometimes collaborate to 582

14 compensate for infrastructure restrictions (sharing / substituting laboratory space or sports facilities) or to handle local events such as organizing conferences, cultural events or distinct projects outside their core activities. One case explicitly states to nurture / foster relations to a neighbour institution in order to benefit from lessons learned in each others domain or to collaborate in regard to administrative issues. Another case illustrates a very particular type of collaboration, as their core activity is the training of future pedagogues and teachers, who - naturally - need to train their skills in other schools. We could not identify relations to suppliers as understood in industrial contexts. However, this driver and its various definitions constitute a perfect example of what we understand with the analogy of value chain in education Relations to suppliers and service providers (Beziehungen zu Lieferanten und Dienstleistern) Only four of 12 case studies cover this dimension that connects an educational institution to its environment and keeps its physical structures as well as maintenance or access to services alive. This is another interesting deviation from ICS reports form typical SMEs (Alwert et al 2010), as most of them nurture relations to their key suppliers. Maybe we could explain this difference with the relative autonomy of most educational institutions that do not really depend on external service providers in a similar way as do (industrial) enterprises Relations to Authorities (Beziehungen zu Aufsichtsbehörden) This category is covered by only three out of 12 cases. However, it might be wrong to imply a relaxed atmosphere between schools and their governing authorities. Rather, this indicates a relevant part of the systems borders that sometimes get blurred in daily routines. However, the teachers - in contrast to administrative staff - are rarely in direct contact with the authorities but still might be very aware of the external influence Relations to the public (Beziehungen zur Öffentlichkeit) This category is covered by 8 of 12 case studies and shows only very little semantic deviation. Public relations and relations to the public are extremely close. Both terms indicate the awareness of the participants of the relative impact of their activities on the public prestige. But given the relative small size of the majority of local educational institutions (schools have typically less than 1000 pupils and less than 50 teachers), they are depending on public budgets but in turn have little to no influence on the federal budgeting process, for example via an electorate. After inquiring deeper, we identified considerable competition among various schools to attract pupils and students and thus secure the teacher s jobs. This, of course, applies primarily to urban areas, where geographical distances between competing institutions are smaller. 583

15 3.1.6 Relations to Investors (Beziehungen zu Kapitalgeber und Investoren) This is an interesting driver for two reasons: it is a default driver in both, the guideline and the toolbox, whose primary focus is on SMEs and enterprises, who have (weak) ties to the capital market. This is regularly not the case for schools, and only one group suggested it. The authors of this case did not provide a convincing rationale for including this driver in their analysis. Hence, it servers as controlling variable for the students ambition to come up with education-specific drivers for Intellectual Capital as well as to accept the other 11 cases as qualitatively reasonable. But we probably can learn another lesson from this (minor) deviation: users tend to use what is already available. They readily accept whatever standard is proposed and subordinate (maybe not only in educational systems) to more or less meaningful default settings. When investigating a sector such as education with an alien concept that originally was developed in a for-profit-environment of industry and service providing organizations, we must be very careful to premature adoption of established truth or standards. After reviewing 12 case studies of Intellectual Capital Statements in educational systems, we can conclude that there is a widespread understanding for Intellectual Capital drivers in general. We can document a huge variety of terminology with slightly differing focus and interpret this as a signal for the need of a distinct terminology in educational systems that support contextual understanding, meaning and governance. However, we could demonstrate a semantic consolidation of various drivers of Intellectual Capital to more general clusters. We would not suggest that this aggregation is supporting a (simple) benchmarking of results, as the understanding and interpretation of the participants are diverse. With this analysis, we have to conclude this chapter with the observation that currently, we do not have an established set of drivers for Intellectual Capital. But we have candidates that might qualify for a future recommendation. 3.2 Can we construct a value chain perspective? Postulating a connection between consecutive schools might appear quite obvious. However, so far we could identify only few international examples of education providers that collaborate along a progression path with the explicit understanding to advance the career of their students. Among them are Anglo-American based feeder schools (WSJ, 2007) for the Ivy League universities that actively communicate their mission to serve as feeder schools. Interestingly, all of them are private schools with a different organizational structure than are established in Austrian schools. So, we would like to investigate the phenomenon of non-integration in the following paragraphs. The 12 case studies contain data of cross-impacts for each driver of Intellectual Capital as well as on the core processes and results. The processes are quite diverse. However, a common pattern can be identified as a minimum common denominator. All education institutions have a process for knowledge transfer (education in a wide interpretation), a process for administrative agendas (such as managing the organization) and sometimes a (not very distinct) process for facility management in a wider sense. 584

16 More interesting from a value chain perspective are the outputs of the processes, the results of educational organizations. Applying the industrial concept of value chains as implemented in the aviation and automotive industry (Porter 1985) to educational systems as described in Figure 2, and combining them with concepts of system dynamics (Sterman, 2000, Vester 1999) and Intellectual Capital Management (Bornemann et. al 2007), we could define a cross-impact matrix that takes advantage of the data we can obtain from our case studies. Wissensbilanz - made in Germany applies these ideas and uses an interdependency matrix that relates drivers of Intellectual Capital to core processes and strategic objectives (=business results). Figure 3 illustrates the structure. GP- GP- GP- GE- GE- HK- HK- HK- HK- SK- SK- SK- SK- BK- BK- BK- BK- Active sum GP-1 Unterrichtsablauf x GP-2 Wissenstransfer 1 x GP-3 Individualisierung 0 2 x GE-1 Image/Kundenzufriedenheit x GE-2 Anzahl der Einschreibungen x HK-1 Fachkompetenz x HK-2 Soziale Kompetenz x HK-3 Motivation der LehrerInnen x HK-4 Didaktische und Methodenkompetenz x SK-1 Kooperation und Wissenstransfer x SK-2 Informationstechnik x SK-3 Unternehmenskultur x SK-4 Wissensobjekte x BK-1 Kundenbeziehungen x BK-2 Beziehung zu Volksschulen im 20. Bezirk x BK-3 Beziehungen zur Öffentlichkeit x 1 11 BK-4 Beziehungen zu Kooperationspartnern x 15 passiv sum Figure 3: example of the IC matrix of one education provider from the second level The values in the matrix have the following connotation: 0 - a change in the driver of the first column has no effect a change in the driver of the first column has weak effect 2 - a change in the driver of the first column has proportional effect 3 - a change in the driver of the first column has exponential effect on the other drivers in the matrix. Generally, these matrixes are heterogeneous. This reflects the differences in organizational structure, organizational maturity, culture and general orientation of the institutions. In this article, we will not discuss internal management issues - they are discussed in Bornemann but focus on the external connections that might support our interpretation of comprehensive education as a value chain. This matrixes provide deep insight into the context of an organization and allow to observe, how the members of an organization understand the cross dependencies of strategy-relevant drivers on each other and, ultimately, on the strategic objectives. We can aggregate the data for each level and reconstruct a value chain for the education system in order to better understand the interdependencies between intangible assets of the whole sector as well as the relations between different levels of education. Additionally, we could learn about governing interventions into the system - and probably derive some learning s that are interesting for designing policy instruments. The matrix of on educational institution (see Figure 3) is represented in one of the grey fields. 585

17 Pre-School Primary Secondary Tertiary Quartary HC SC RC BP BR HC SC RC BP BR HC SC RC BP BR HC SC RC BP BR HC SC RC BP BR Pre-School Primary Secondary Tertiary Quartary HC SC RC BP BR HC SC RC BP BR HC SC RC BP BR HC SC RC BP BR HC SC RC BP BR Figure 4: Integrated system perspective of the value chain From Figure 4 we see that according to our understanding of an educational system that accompanies students from early childhood to a job in a knowledge based organization, the output of the first level (primary schools) becomes the input for the second level (secondary schools). Graduates from secondary level become input for the learning processes of the tertiary level and so on. As we are constructing this concept, we see that the data based on case studies with focus on one organization rarely cover impacts on other systems. However, some drivers of Relational Capital and some drivers describing the strategic objectives do support this assumption (highlighted in Figure 4): We identified one example of Improving relations to a (specific) school in the same district as being part of the narrative of Relational Capital. We found one example where improving relations to the next higher educational institutions constitutes an explicit strategic objective. And, as suggested in the hypothesis, we found six cases with improving the qualifications of students as being an explicit strategic objective. As an intermediate summary, we can confirm the availability of connectors between individual institutions along a value chain. However, this is not valid for all covered case studies. We can not document an established and comprehensive value chain as e.g. in the automotive industries. Figure 4 suggests how a formal complete description of the value chain could be established. It illustrates how an improvement of Intellectual Capital as a major resource - apart from financial means - influences the business processes of each institution. Right now, we can not report homogeneous patterns, but highly individual perceptions of interdependencies. 586

18 3.3 Can we assume typical patterns in similar organizations? Even though we have only a very small sample of case studies available, we tested for similarities and differences in the cross-impact matrices of various institutions. A simple comparison of Figure 3 and Figure 5 demonstrates the huge variety. GP-1 GP-2 GP-3 GE-1 GE-2 HK-1 HK-2 HK-3 SK-1 SK-2 SK-3 BK-1 BK-2 BK-3 active sum GP-1 Fort- und Weiterbildungskonzept x GP-2 Unterrichtsmethodik 1 x GP-3 alternative Formd der Leistungsrückmeldung 2 3 x GE-1 Kundenbindung x GE-2 Image x HK-1 MitarbeiterInnenqualifikation x HK-2 Selbst- und Sozialkompetenz x HK-3 Authentisches Chrsitsein x SK-1 Produktinnovation x SK-2 Kooperations- und Kommunikationsformen innerhalb der Schule x SK-3 Wissenstransfer und Wissenssicherung x BK-1 Beziehungen zu Eltern und Erziehungsberechtigten x BK-2 Beziehungen zur Öffentlichkeit x 2 18 BK-3 Beziehungen zu Kooperationspartnern x Figure 5: example of the IC matrix of one education provider from the second level The first matrix (Figure 3) contains an almost equal distribution, the later (Figure 5) a rather extreme frequency of exponential effects. This can be explained by very different maturity levels or structural change in strategy and the organization. The second case represents such a situation: it is a pilot organization for a completely new set up education model. As a consequence of the restructuring, changes, even small ones, have still a remarkable impact on the whole system. However, with the distance of desktop research analysis, the case certainly represents an extreme one and would be realistic only for a very short period of time, until change efforts will be stabilized. Table 3: dynamics of various educational institutions within the same level level 2 seminar 1 seminar 2 seminar 3 master 1 Master 2 matrixmax system dynamic 0,33 0,35 0,41 0,72 0,89 We tested for the dynamics of organizations with a simple measure that relates the maximum energy of a system the actual value of the matrix. The maximum is calculated by the number of drivers times the number of drivers minus one (there is no impact on the factor itself) times 3 as the maximum score. What we see from Table 3 is a strong spread of dynamic in various systems. This indicates some challenges in simply comparing the systems without taking into account for the fundamental assumptions and the context in the individual organizations. Thus, we can not assume typical patterns of organizations, not even within one level (here, the secondary level) of the education value chain. However, we could use these 12 cases as prototypes to identify some further questions and formulate initial learnings. 587

19 4 Discussion Applying methodologies for Intellectual Capital Management in educational institutions and integrating the management of these resources along the value chain of education seems to be very beneficial not only for ministries of education as the major common stakeholder but for de-central decision makers as well. Prioritizing scarce resources and systematically observing intangible assets in public as well as privately management educational intuitions contributes to economic improvement and better accomplishment of strategic objectives. If we can construct a value chain similar to industrial value chains in education, then processes to integrate the organizations and to streamline the education processes should be possible. This assumes, however, a shared understanding of the final objective, for example serving a customer need. According to our findings ( Fachkompetenz 6 Fachliche Qualifikation (inkl. Weiterbildung) 1 Fachliche Kompetenz 3 Methodisches und didaktisches Fachwissen der Lehrer 1 Didaktische und Methodenkompetenz 1 Qualifikation der Lehrkräfte 1 MitarbeiterInnenqualifikation 1 Erfahrung der Lehrkräfte 1 Fachkompetenz und method. Kompetenz 1 Fortbildung 1 allgemeine Mitarbeiterkompetenzen entwickeln 1 Fachqualifikationen für die Akademie aufbauen 1 FACHKOMPETENZ 19 Mitarbeitermotivation und Zufriedenheit 1 Motivation der LehrerInnen 1 Mitarbeitermotivation 3 Motivation 1 Motivation der Lehrkräfte 1 Mitarbeiterzufriedenheit 2 Identifikation 1 Engagement der Lehrer 1 Engagement der Erzieher 1 MITARBEITERMOTIVATION 12 soziale Kompetenz 5 Sozialkompetenz 4 Selbst- und Sozialkompetenz 1 Soft Skills 1 Sozialkompetenz der Lehrkräfte 1 SOZIALKOMPETENZ

20 Führungskompetenz 5 Führungskompetenz weiterentwickeln 1 Führungsstärke der Direktion und der Lehrer 1 Führungsstärke der Erzieher 1 FÜHRUNGSKOMPETENZ 8 Persönliche Einstellung 1 Authentisches Christsein 1 Veränderungsfähigkeit unterstützen 1 SONSTIGE 3 and Table 5), it can not be assumed that all players in the value chain have a shared understanding, who their actual customer is and what the customers primary demands might look like. The value chain perspective comes not naturally from the so far highly autonomous educational institutions. We need to define the linking pins between the systems which are already visible, but not yet established. Their impact on the education system as a whole would be considerable. We added the perspective of Intellectual Capital, not only as a resource and input, but - simultaneously - the intangible output of education systems as well. Given the complex and knowledge based processes of education and the nature of knowledge transfer, training and feedback, there might be plenty of room for further research to better understand the management process of educational institutions. Another challenge relates to the governance process itself. Of course, the education systems in general and the education system in Austria in particular, are subject of heavy regulation. But these regulations regularly are influenced by various stakeholders such as (competing) teacher unions, political parties and government administration. As a consequence, the governance structures are quite complex and not prone to managerial optimization methods which are established in the industry. Finally, when reflecting on the process of Intellectual Capital Reporting and the people involved - our cases covered more than 55 people for several days - we have to acknowledge their desire for transparency in order to align their individual behavior and daily decision making to a comprehensive education process, ideally tailored for each pupil or student. This seems to be extraordinarily difficult, given the complexity of the system, the subtle differences and the context dependency. 5 Outlook There seems to emerge a growing demand of more entrepreneurial approaches especially in tertiary educational governance as a consequence of enhanced (international) competition and limited public resources and funding. Thus the implementation of a strategic management processes in education provides as future opportunity. Intellectual Capital Management provides an effective methodology to support such governance mechanisms in a participatory manner. Intellectual Capital concepts are established in 589